import pickle
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from tqdm import tqdm
import matplotlib.pyplot as plt

# 加载数字数据集
digits = load_digits()
X = digits.data
y = digits.target

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn_model = None

# 初始化一个列表以存储每个k值的准确率
accuracies = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41), desc="Training KNN models"):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    accuracies.append(accuracy)
    
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn_model = knn

# 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as f:
    pickle.dump(best_knn_model, f)

# 打印最佳准确率和相应的k值
print(f"Best accuracy: {best_accuracy}")
print(f"Best k value: {best_k}")

# 绘制准确率曲线
plt.plot(range(1, 41), accuracies, marker='o')
plt.title('KNN Varying number of neighbors')
plt.xlabel('Number of neighbors')
plt.ylabel('Accuracy')
plt.axvline(x=best_k, color='r', linestyle='--')
plt.text(best_k, best_accuracy, f'Best K={best_k}, Accuracy={best_accuracy}', color='red')
plt.savefig('accuracy_plot.pdf')
plt.show()